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122 results for “Brain dynamics”
Animal data associated with: Pattern dynamics and stochasticity of the brain rhythms
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Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording
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Computerized analysis of brain MRI parameters dynamics in young patients with Cushing Syndrome – a case-control study
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The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized Placebo-Controlled Clinical Trial
<p>The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (<a href="https://zenodo.org/deposit/1308615">https://zenodo.org/deposit/1308615</a>); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol</p>
Data from: Brain states govern the spatio-temporal dynamics of resting-state functional connectivity
<p>Previously, using simultaneous resting-state functional magnetic resonance imaging (fMRI) and photometry-based neuronal calcium recordings in the anesthetized rat, we identified blood oxygenation level-dependent (BOLD) responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized correlate of locally recorded neuronal activity (<a href="#_ENREF_68" title="Schwalm, 2017 #1"><span>Schwalm et al., 2017</span></a>). Here, using the same techniques, we investigate two distinct cortical activity states: persistent activity, in which compartmentalized network dynamics were observed; and slow wave activity, dominated by a cortex-wide BOLD component, suggesting a strong functional coupling of inter-cortical activity. During slow wave activity we find a correlation between the occurring slow wave events and the strength of functional connectivity between different cortical areas. These findings suggest that down-up transitions of neuronal excitability can drive cortex-wide functional connectivity. This study provides further evidence that changes in functional connectivity are dependent on the brain's current state, directly linked to the generation of slow waves.</p> <p> </p>
Dataset related to 'Pedagogy matters: Experience-dependent spatiotemporal brain network dynamics'
<p>System Diversity (SD) and Spatio-Temporal Diversity (STD) values (as defined in [Vohryzek2020]) estimated on two groups of children (6-12 y.o.) from Montessori and traditional schools. We provide the values for the two groups and values from 1000 permutations between groups.</p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
Dynamic Visualization of ResNet Layer Activations for Brain Health Classification
<p>This GIF file provides a dynamic visualization of the internal representations (activations) from the ResNet 18 model layers (2 to 69) during a brain health classification task. The sequence begins by showing the original input image, followed by successive activation maps visualized using the "jet" colormap. Each frame corresponds to the activations extracted from a specific layer in the ResNet, resized to match the input image dimensions for better interpretability. </p> <p>The dataset used for this visualization is from S. Bhuvaji, "Brain Tumor Classification MRI," published on Kaggle in 2023. This dataset contains MRI images of brain tumors and has been utilized to train and evaluate the ResNet model for the classification of brain health states. The input sample displayed in the GIF is one such MRI image from the dataset, highlighting the model's ability to extract and analyze features relevant to brain tumor diagnosis.</p> <p>The activations reveal how the ResNet processes the input image hierarchically. In the <strong>early layers (e.g., Layers 2-10)</strong>, the network preserves much of the spatial structure of the original image, focusing on edges and low-level features. Moving to the <strong>intermediate layers (e.g., Layers 11-40)</strong>, the network begins to emphasize localized patterns while filtering out irrelevant structures such as the skull, concentrating instead on regions associated with tumors or health-related features. Finally, the <strong>deep layers (e.g., Layers 41-69)</strong> extract highly abstract and classification-relevant patterns, concentrating on tumor-related features while discarding most of the background.</p> <p>The progression of the activations in the GIF demonstrates how the network transitions from general image features to highly specialized, diagnostic features that are critical for the classification task. This visualization helps provide an intuitive understanding of the hierarchical processing capabilities of convolutional neural networks (CNNs) in medical image analysis.</p> <h3>Key Features:</h3> <p>The visualization includes an input brain image and its corresponding activation maps, extracted from each ResNet layer. The activation maps are resized to match the original image for consistency, and the "jet" colormap is applied to enhance visual interpretation of activation intensities. Each frame in the GIF dynamically updates to show the activations of the next layer, offering an engaging representation of the network's internal behavior.</p> <h3>Use Cases:</h3> <p>This GIF is a valuable resource for education, research, and presentations. It can be used to illustrate how deep learning models process medical images, providing insights into the hierarchical feature extraction process. Researchers and educators can leverage this visualization to explain the concept of feature abstraction in CNNs. It is also ideal for inclusion in talks, posters, and papers to showcase the dynamic analysis of neural network activations.</p>
Data from: Dynamics in brain activation and behaviour in acute and repeated social defensive behaviour
<p>In nature, confrontations between conspecifics are recurrent and related, in general, due to the lack of resources such as food and territory. Adequate defences against a conspecific aggressor are essential for the individual's survival and the group integrity. However, repeated social defeat is a significant stressor promoting several behavioural changes including social defence per se. What would be the neural basis of these behavioural changes? To build new hypotheses about this, we here investigate the effects of repeated social stress on the neural circuitry underlying motivated social defence behaviour in male mice. We observed that animals re-exposed to the aggressor three times spent more time in passive defence during the last exposure than in the first one. These animals also show less activation of the amygdalar and hypothalamic nuclei related to the processing of conspecific cues. In turn, we found no changes in the activation of the hypothalamic dorsal pre-mammillary nucleus (PMD) that is essential for passive defence. Therefore, our data suggest that the balance between the activity of circuits related to conspecific processing and the PMD determines the pattern of social defence behaviour. Changes in this balance may be the basis of the adaptations in social defence after repeated social defeat.</p>
Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding
<p>This is the data and script that reproduces the results of the paper:<br> Kato et al., PNAS 2022, Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding.<br> As the results of each analysis that are needed for other analysis are also contained in this folder, <br> each sub-section of the script can be run independently (e.g., you can plot OERP waveforms or GFPs without executing preprocessing). <br> Refer to README.txt for detailed discription.</p>
Time Dependent Analysis of Rat Microglial Markers in Traumatic Brain Injury Reveals Dynamics of Distinct Cell Subpopulations
<p>The repository contains seven zip files for flow cytometry data for rat microglia following Controlled cortical impact (CCI) collected at seven time points: 3 hours, 1 day, 2 days, 7 days 14 days, 21 days and 28 days.</p> <p>Each file contains 24 CSV files converted from the raw fcs file and text files that contain meta data about the file.</p> <p>Each file name depicts the information of whether it is Panel A (M1 in the file name) or Panel B (M2 in the file name) measurement and whether the measurement was conducted on the ipsilateral (ipsi) or contralateral (contra) side of the CCI, or sham conducted on the ipsilateral (ipsi) or contralateral (contra) sides.</p> <p>Below is the description of the columns of each file:</p> <table> <tbody> <tr> <td> <p><strong>File column name</strong></p> </td> <td> <p><strong>Original name</strong></p> </td> <td> <p><strong>Panel A (M1) context</strong></p> </td> <td> <p><strong>PAnel B (M2) context</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>F0</p> </td> <td> <p>FSC-A</p> </td> <td> <p>FSC-A</p> </td> <td> <p>FSC-A</p> </td> <td> <p>Forward scattering Area</p> </td> </tr> <tr> <td> <p>F1</p> </td> <td> <p>FSC-H</p> </td> <td> <p>FSC-H</p> </td> <td> <p>FSC-H</p> </td> <td> <p>Forward scattering Height</p> </td> </tr> <tr> <td> <p>F2</p> </td> <td> <p>FSC-W</p> </td> <td> <p>FSC-W</p> </td> <td> <p>FSC-W</p> </td> <td> <p>Forward scattering Width</p> </td> </tr> <tr> <td> <p>F3</p> </td> <td> <p>SSC-A</p> </td> <td> <p>SSC-A</p> </td> <td> <p>SSC-A</p> </td> <td> <p>Side scattering area</p> </td> </tr> <tr> <td> <p>F4</p> </td> <td> <p>FITC-A</p> </td> <td> <p>FITC-A</p> </td> <td> <p>FITC-A</p> </td> <td> <p>Fluorescein isothiocyanate</p> </td> </tr> <tr> <td> <p>F5</p> </td> <td> <p>PE-A</p> </td> <td> <p>CD32</p> </td> <td> <p>CD200R</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F6</p> </td> <td> <p>PerCP-Cy5-5-A</p> </td> <td> <p>-</p> </td> <td> <p>CD163</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F7</p> </td> <td> <p>PE-Cy7-A</p> </td> <td> <p>CD11</p> </td> <td> <p>CD11</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F8</p> </td> <td> <p>BV421-A</p> </td> <td> <p>P2Y12</p> </td> <td> <p>P2Y12</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F9</p> </td> <td> <p>BV510-A</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> <td> <p>Dye </p> </td> </tr> <tr> <td> <p>F10</p> </td> <td> <p>Alexa Fluor 647-A</p> </td> <td> <p>CD86</p> </td> <td> <p>RT1B</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F11</p> </td> <td> <p>APC-Cy7-A</p> </td> <td> <p>CD45</p> </td> <td> <p>CD45</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F12</p> </td> <td> <p>Event Time</p> </td> <td> <p>Event Time</p> </td> <td> <p>Event Time</p> </td> <td>Event Time</td> </tr> </tbody> </table>
Neural Dynamics and Connectivity in Response Inhibition and Traumatic Brain Injury
ClinicalTrials.gov study NCT01194661. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Multimodal Investigation of Cortico-Basal Ganglia-Thalamo-Cortical Network Dynamics in Dystonic Patients with Deep Brain Stimulation
ClinicalTrials.gov study NCT06716983. IPD Sharing: Not stated. Countries: 1. Publications: 13.
The Role of Sex Steroids and Serotonin Brain Dynamics in Perinatal Mental Health
ClinicalTrials.gov study NCT03795688. IPD Sharing: YES. Countries: 1. Publications: 11.
Spatial and Dynamic Characterization of Brain Activity for Language and Posture (Verticality) During Normal Aging. Magnetoencephalography (MEG) Study (MEG-AGING)
ClinicalTrials.gov study NCT04036162. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Whole-Brain Dynamics in the Natural Menstrual Cycle vs. an Ovarian Stimulated Cycle: Impact of Hormonal Fluctuations on Healthy Women Undergoing Ovarian Stimulation
ClinicalTrials.gov study NCT06910293. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Dynamic and Static Autoregulation After Brain Injury
ClinicalTrials.gov study NCT01069250. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Re-Step: Dynamic Balance Treatment of Gait for Acquired Brain Injury (ABI) Victims
ClinicalTrials.gov study NCT02215590. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Influence of General Anesthesia on the Dynamic Changes in Brain Damage Markers During and After Craniosynostosis Operations in Infancy
ClinicalTrials.gov study NCT05911139. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Dynamic F-DOPA PET for Differential Diagnosis Between Recurrence and Radionecrosis of Brain Metastasis
ClinicalTrials.gov study NCT05762172. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.